library(tidyverse)
rOceanData is a package currently in development with
the aim of making satellite data easily accessible to researchers and
stakeholders (managers, conservation groups, researchers).
rOceanData accesses a wide range of ocean variables
(e.g. sea surface temperature, chlorophyll, salinity) via built-in lists
or via the ERDAPP
/griddap servers.
The rOceanData workflow follows three steps: 1) extract
data, 2) summarise data (e.g. yearly means), and 3) visualise data
(static maps, interactive maps, animations):

The package is currently in development, and below are examples of basic functions:
rOceanData is an R package in development with the aim of simplifying access to satellite data on ocean health.
The space argument can either be an area of ocean with
determined by corner points (xmin, xmax, ymin, ymax), for example:
maldives_data <- extract_ocean_data("NOAA_DHW",
space = c(72, 74.5, -1.5, 7.5),
time = c("1990-01-01", "2020-01-01"))
Alternatively, if you need data for a particular set of sites with
GPS points, upload an excel file with the longitude and latitude and
extract_ocean_data will extract only the data for the
corresponding points
site_sst_data <- extract_ocean_data(space = "files/sites/sitesurveys.xls", time = c("1990-01-01", "2020-02-01"))
To check the space prior to downloading the data, use
the check_space function:
check_space(space = c(72, 74.5, -1.5, 7.5))
distil /dɪˈstɪl/ - “extract the essential meaning or most important aspects of”
Once the data is extracted, distil_ocean_data() is the
second step of the workflow where data can be averaged through space
(e.g. reducing the resolution from 1km * 1km to say… 5 * 5km resolution
to meaningfully compare datasets) or through time (e.g. finding the
average monthly data from a daily dataset).
For example, to get the mean SST for each gridcell across the entire time-series:
maldives_sst_mean <- maldives_data %>%
distill_ocean_data(calculate=mean)
Or to get the mean maximum monthly SST for each gridcell :
maldives_sst_mmm <- maldives_data %>%
distill_ocean_data(group="month", calculate=max)
Or to get the mean annual SST for each gridcell:
maldives_sst_annual_mean <- maldives_data %>%
distill_ocean_data(group="year", calculate=mean)
Map ocean data takes the summarised data from distill_ocean_data and generates static maps:
maldives_sst_mean %>%
map_ocean_data()

Alternatively, plot the annual means for each year in the time-series
by specifying by=
maldives_sst_annual_mean %>%
map_ocean_data(by="year")

Interactive ocean data takes the summarised data from distill_ocean_data and generates interactive maps:
maldives_sst_mean %>%
interactive_ocean_data()